Assessing Pilot Workload During Takeoff and Climb Under Different Weather Conditions: A fNIRS-Based Modeling Using Deep Learning Algorithms
Bibliographic record
Abstract
Excessive workload caused by weather conditions may increase pilot errors and flight risk. The assessment of pilot workload using pilots’ physiological data is a potential method to address this issue. We conducted a flight simulator study involving 18 cadet pilots with real flight experiences ranging from 232 to 250 h and used noninvasive functional near-infrared spectroscopy (fNIRS) to measure the pilots’ brain activity. Pilots’ subjective National Aeronautics and Space Administration task load index ratings of workload were also recorded. The tested flight maneuvers included a total of 54 takeoff and climb tasks under different weather conditions. Over 1100 features covering three hemoglobin signals and four brain cortexes were extracted from the fNIRS data. A statistical analysis was carried out on the subjective ratings and fNIRS features, and a weighting analysis was applied to the selected fNIRS features that were highly sensitive to pilot workload levels. A convolutional neural network (CNN) with an attention mechanism (CNN-Attention) was built as a classifier for assessing pilot workload and compared with CNN, deep neural network, extreme gradient boosting, and random forest models. The results suggested that pilot workload was highly associated with three hemoglobin measures as well as the activities of different brain regions, including the prefrontal cortex, motor cortex, and occipital cortex, and the CNN-Attention performed best. The findings from this study could provide a reference for optimizing pilot training systems and improving flight safety under different weather conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".